We present a new formalism for the treatment and understanding of multispectral imags and multisensor fusion based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. In particular, we consider multiple registered visualization of multi-modal medical imaging. We demonstrate how our methodology can reveal significantly more interpretive information to a radiologist or image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm performs better. A variety of experimental results from medical imagin and remotely sensed data are presented.